tf.data.IteratorSpec
Type specification for tf.data.Iterator.
Inherits From: TypeSpec
tf.data.IteratorSpec(
element_spec
)
For instance, tf.data.IteratorSpec can be used to define a tf.function that takes tf.data.Iterator as an input argument:
@tf.function(input_signature=[tf.data.IteratorSpec(
tf.TensorSpec(shape=(), dtype=tf.int32, name=None))])
def square(iterator):
x = iterator.get_next()
return x * x
dataset = tf.data.Dataset.from_tensors(5)
iterator = iter(dataset)
print(square(iterator))
tf.Tensor(25, shape=(), dtype=int32)
| Attributes | |
|---|---|
element_spec |
A nested structure of TypeSpec objects that represents the type specification of the iterator elements. |
value_type |
The Python type for values that are compatible with this TypeSpec. In particular, all values that are compatible with this TypeSpec must be an instance of this type. |
Methods
from_value
@staticmethod
from_value(
value
)
is_compatible_with
is_compatible_with(
spec_or_value
)
Returns true if spec_or_value is compatible with this TypeSpec.
most_specific_compatible_type
most_specific_compatible_type(
other
)
Returns the most specific TypeSpec compatible with self and other.
| Args | |
|---|---|
other |
A TypeSpec. |
| Raises | |
|---|---|
ValueError |
If there is no TypeSpec that is compatible with both self and other. |
__eq__
__eq__(
other
)
Return self==value.
__ne__
__ne__(
other
)
Return self!=value.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/data/IteratorSpec